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Added generic storage model (#248)
* Added simple generic storage model * Merging * Updated battery cost model and example * Added simple generic storage model docs * Added test for example * Finishing touches * remove unused cherry-picked file * remove unused cherry-pick test yaml * minor details in example * test generic storage model against H2 storage model * remove super * Added more exact example test values * Updated tests! --------- Co-authored-by: elenya-grant <116225007+elenya-grant@users.noreply.github.com> Co-authored-by: kbrunik <kbrunik@gmail.com>
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‎CHANGELOG.md‎

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- Added `"custom_electrolyzer_cost"` model, an electrolyzer cost model that allows for user-defined capex and opex values
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- Made `pipe` and `cable` substance-agnostic rather than hard-coded for `hydrogen` and `electricity`
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- Change finance handling to use `finance_subgroups` and `finance_groups` defined in the `plant_config` rather than previous `financial_groups` in the `tech_config` and `technologies_to_include_in_metrics` in `plant_config`
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- Added generic storage model, useful for battery, hydrogen, CO2, or other resource storage.
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## 0.3.0 [May 2 2025]
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‎docs/_toc.yml‎

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- file: technology_models/pvwattsv8_solar_pv.md
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- file: technology_models/atb_costs_pv.md
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- file: technology_models/co2.md
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- file: technology_models/simple_generic_storage.md
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- caption: Examples
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chapters:

‎docs/technology_models/simple_generic_storage.md‎

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# Simple Generic Storage Model
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The Simple Generic Storage model provides a flexible framework for modeling various types of energy storage systems in H2Integrate. While particularly useful for battery storage, this model can be used to simulate the storage of different resources including hydrogen, CO2, or any other commodity.
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## Overview
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The Simple Generic Storage model consists of two main components:
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1. **SimpleGenericStorage**: A minimal component that defines the input interface for the storage system
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2. **DemandOpenLoopController**: The core logic component that handles storage operations, state of charge calculations, and resource management
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This architecture allows the storage system to work with any resource type by simply configuring the resource name and units, making it quite versatile.
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## Example Applications
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### Battery Storage (Example 19)
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Example 19 demonstrates a wind-battery dispatch system that showcases the Simple Generic Storage model in action. This example:
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- Models a wind farm providing variable electricity generation
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- Uses battery storage with defined capacity and charge/discharge rates
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- Implements demand-based control with a constant electricity demand
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- Demonstrates realistic battery operations including state of charge management and curtailment
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The example produces detailed plots showing:
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- Battery state of charge over time
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- Electricity flows (input, output, curtailed, missed load)
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- How the storage system balances variable wind generation with constant demand
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### Hydrogen Storage
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The model can be configured for hydrogen storage systems by setting:
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```yaml
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resource_name: "hydrogen"
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resource_rate_units: "kg/h"
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max_capacity: 1000.0 # kg
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```
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This is useful for modeling hydrogen production from electrolyzers with variable renewable input and steady hydrogen demand for industrial processes.
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### CO2 Storage
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For carbon capture and utilization systems:
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```yaml
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resource_name: "co2"
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resource_rate_units: "kg/h"
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max_capacity: 50000.0 # kg
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```
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This enables modeling of CO2 capture systems with temporary storage before utilization or permanent sequestration.

‎docs/technology_models/technology_overview.md‎

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| :---------------- | :---------------: |
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| `h2_storage` | hydrogen |
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| `battery` | electricity |
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| `generic_storage` | Any |
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(controller)=
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## Controller
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+ `'hydrogen_tank_performance'`
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- cost models:
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+ `'hydrogen_tank_cost'`
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- `generic_storage`: any resource storage
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- `battery`: battery storage
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- cost models:
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+ `'atb_battery_cost'`
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## Controller Models
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- `pass_through_controller`

‎examples/14_wind_hydrogen_dispatch/hydrogen_dispatch.ipynb‎

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],
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"metadata": {
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"kernelspec": {
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"display_name": "h2integrate-mcm",
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"display_name": "hopp",
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"language": "python",
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"name": "python3"
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},
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.12"
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"version": "3.11.13"
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}
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},
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"nbformat": 4,

‎examples/19_wind_battery_dispatch/driver_config.yaml‎

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name: "driver_config"
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description: "Runs a wind plant and battery with a demand open loop controller"
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general:
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folder_output: wind_plant_run

‎examples/19_wind_battery_dispatch/plant_config.yaml‎

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name: "plant_config"
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description: "This plant is located in MN, USA..."
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site:
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latitude: 47.5233
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longitude: -92.5366
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# array of polygons defining boundaries with x/y coords
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boundaries: [
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{
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x: [0.0, 1000.0, 1000.0, 0.0],
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y: [0.0, 0.0, 100.0, 1000.0],
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},
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{
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x: [2000.0, 2500.0, 2000.0],
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y: [2000.0, 2000.0, 2500.0],
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}
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]
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plant:
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plant_life: 30
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# array of arrays containing left-to-right technology
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# interconnections; can support bidirectional connections
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# with the reverse definition.
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# this will naturally grow as we mature the interconnected tech
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technology_interconnections: [
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["wind", "battery", "electricity", "cable"],
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# etc
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]
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finance_parameters:
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finance_groups:
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finance_model: "ProFastComp"
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model_inputs:
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params:
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analysis_start_year: 2032
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installation_time: 36 # months
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inflation_rate: 0.0 # 0 for nominal analysis
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discount_rate: 0.09 # nominal return based on 2024 ATB baseline workbook for land-based wind
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debt_equity_ratio: 2.62 # 2024 ATB uses 72.4% debt for land-based wind
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property_tax_and_insurance: 0.03 # percent of CAPEX estimated based on https://www.nrel.gov/docs/fy25osti/91775.pdf https://www.house.mn.gov/hrd/issinfo/clsrates.aspx
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total_income_tax_rate: 0.257 # 0.257 tax rate in 2024 atb baseline workbook, value here is based on federal (21%) and state in MN (9.8)
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capital_gains_tax_rate: 0.15 # H2FAST default
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sales_tax_rate: 0.07375 # total state and local sales tax in St. Louis County https://taxmaps.state.mn.us/salestax/
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debt_interest_rate: 0.07 # based on 2024 ATB nominal interest rate for land-based wind
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debt_type: "Revolving debt" # can be "Revolving debt" or "One time loan". Revolving debt is H2FAST default and leads to much lower LCOH
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loan_period_if_used: 0 # H2FAST default, not used for revolving debt
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cash_onhand_months: 1 # H2FAST default
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admin_expense: 0.00 # percent of sales H2FAST default
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capital_items:
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depr_type: "MACRS" # can be "MACRS" or "Straight line"
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depr_period: 5 # 5 years - for clean energy facilities as specified by the IRS MACRS schedule https://www.irs.gov/publications/p946#en_US_2020_publink1000107507
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refurb: [0.]
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cost_adjustment_parameters:
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cost_year_adjustment_inflation: 0.025 # used to adjust modeled costs to target_dollar_year
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target_dollar_year: 2022
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finance_subgroups:
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electricity:
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commodity: "electricity"
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technologies: ["wind","battery"]

‎examples/19_wind_battery_dispatch/run_wind_battery.py‎

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import matplotlib.pyplot as plt
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from h2integrate.core.h2integrate_model import H2IntegrateModel
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h2i = H2IntegrateModel("wind_battery_dispatch.yaml")
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# Run the model
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h2i.run()
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h2i.post_process()
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# Battery dispatch plotting
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model = h2i
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fig, ax = plt.subplots(2, 1, sharex=True)
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start_hour = 0
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end_hour = 200
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total_time_steps = model.prob.get_val("battery.electricity_soc").size
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demand_profile = [
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model.technology_config["technologies"]["battery"]["model_inputs"]["control_parameters"][
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"demand_profile"
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]
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* 1e-3
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] * total_time_steps
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ax[0].plot(
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range(start_hour, end_hour),
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model.prob.get_val("battery.electricity_soc", units="percent")[start_hour:end_hour],
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label="SOC",
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linewidth=2,
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)
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ax[0].set_ylabel("SOC (%)")
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ax[0].set_ylim([0, 110])
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ax[1].plot(
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range(start_hour, end_hour),
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model.prob.get_val("battery.electricity_in", units="MW")[start_hour:end_hour],
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linestyle="-",
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label="Electricity In (MW)",
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linewidth=2,
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)
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ax[1].plot(
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range(start_hour, end_hour),
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model.prob.get_val("battery.electricity_curtailed", units="MW")[start_hour:end_hour],
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linestyle=":",
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label="Electricity Curtailed (MW)",
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linewidth=2,
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)
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ax[1].plot(
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range(start_hour, end_hour),
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model.prob.get_val("battery.electricity_missed_load", units="MW")[start_hour:end_hour],
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linestyle=":",
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label="Electricity Missed Load (MW)",
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linewidth=2,
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)
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ax[1].plot(
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range(start_hour, end_hour),
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model.prob.get_val("battery.electricity_out", units="MW")[start_hour:end_hour],
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linestyle="-",
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label="Electricity Out (MW)",
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linewidth=2,
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)
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ax[1].plot(
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range(start_hour, end_hour),
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demand_profile[start_hour:end_hour],
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linestyle="--",
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label="Electricity Demand (MW)",
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linewidth=2,
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)
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ax[1].set_ylabel("Electricity Hourly (MW)")
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ax[1].set_xlabel("Timestep (hr)")
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plt.legend(ncol=2, frameon=False)
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plt.tight_layout()
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plt.show()

‎examples/19_wind_battery_dispatch/tech_config.yaml‎

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name: "technology_config"
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description: "This plant has wind feeding into an electrolyzer with optimization"
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technologies:
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wind:
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performance_model:
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model: "wind_plant_performance"
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cost_model:
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model: "wind_plant_cost"
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resource:
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type: "pysam_wind"
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wind_speed: 9.
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model_inputs:
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shared_parameters:
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num_turbines: 2
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turbine_rating_kw: 8300
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performance_parameters:
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rotor_diameter: 196.
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hub_height: 130.
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layout_mode: "basicgrid"
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model_name: "pysam"
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model_input_file: null
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layout_params:
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row_D_spacing: 10.
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turbine_D_spacing: 10.
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rating_range_kw: [1000, 9000]
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floris_config: null
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operational_losses: 10.0
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timestep: [1, 60]
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fin_model: "default"
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name: "UtilityScaleWindPlant"
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turbine_name: "2023NREL_Bespoke_8.3MW_196" #turbine model to use
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turbine_group: "onshore"
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override_wind_resource_height: False
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adjust_air_density_for_elevation: True
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cost_parameters:
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cost_per_kw: 1500.0
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cost_year: 2019
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battery:
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performance_model:
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model: "simple_generic_storage"
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cost_model:
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model: "atb_battery_cost"
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control_strategy:
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model: "demand_open_loop_controller"
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model_inputs:
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shared_parameters:
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resource_name: "electricity"
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resource_rate_units: "kW"
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max_charge_rate: 5000.0 # kW/time step
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max_capacity: 30000.0 # kWh
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control_parameters:
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max_charge_percent: 1.0 # percent as decimal
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min_charge_percent: 0.1 # percent as decimal
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init_charge_percent: 0.25 # percent as decimal
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max_discharge_rate: 5000.0 # kW/time step
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charge_efficiency: 1.0 # percent as decimal
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discharge_efficiency: 1.0 # percent as decimal
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demand_profile: 5000
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cost_parameters:
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cost_year: 2022
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energy_capex: 300 # $/kWh
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power_capex: 100 # $/kW
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opex_fraction: 0.02 # percent of capex

‎examples/19_wind_battery_dispatch/wind_battery_dispatch.yaml‎

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name: "H2Integrate_config"
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system_summary: "This reference hybrid plant..."
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driver_config: "driver_config.yaml"
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technology_config: "tech_config.yaml"
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plant_config: "plant_config.yaml"

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